MEDIA PREVIEW
Overview & Result
I used Jev to solve a problem every agent eventually runs into: reading logs. 22.8M lines, and running an LLM on every one would've cost $1,120. Tocsin groups them into 11,812 repeating patterns, then asks Jev about each pattern once. 6 minutes, 64 cents, 123 patterns that actually needed to be looked at. http://github.com/TPAteeq/tocsin The paging policy is just a prompt. You tell it what should wake someone up at 3 am and what's just another log line.
How Jev fits in the loop
- Ingest real-time application state and relevant contextual parameters
- Format the decision problem as a bounded Choice or Noul schema
- Query Jev to receive a typed probability distribution in sub-50ms
- Execute downstream actions or route tasks according to the winning choice
How to reproduce
- Inspect the original showcase and source material at https://github.com/TPAteeq/tocsin
- Verify the bounded prompt and input candidate schema configured for Jev
- Benchmark decision latency and classification accuracy against baseline models
Why this build matters
Demonstrates a real-world, cost-effective implementation of Jev in a search & content classification scenario, replacing expensive generative calls with fast typed decisions.
Reported performance
Reported by authorLatency: Sub-50ms deterministic decision window
Performance metrics and decision latency are reported by the original author and community benchmarks.
Limitations
- Task accuracy is bounded by the precision of the defined candidate choices
- Third-party external dependencies and network latency may affect total workflow duration